The tasks of extracting Frequent Itemsets and Association Rules are fundamental primitives in data mining and database applications. Exact algorithms for these problems exist but require scanning the entire dataset, possibly multiple times. When data is too big to be analyzed in its entirety, and obvious approach is to analyze a sample of the data. The major difficulty in this approach is bounding the probability of under- or over-sampling any one of an unknown number of frequent itemsets. Our work circumvents this issue by applying the statistical concept of VC - dimension to develop a novel technique for providing tight bounds on the sample size that guarantees approximation within user-specified parameters (joint work with M. Riondato) In a subsequent work we extend this technique to a novel randomized parallel algorithm to the problem. Our algorithm achieves near-linear speedup while avoiding costly replication of data. We formulated and implemented the algorithm in the MapReduce parallel computation framework (joint work with M. Riondato, J. DeBrabant and R. Fonseca).

Mental maps from tactile virtual objects

Many sensory substitution interfaces for visually impaired subjects have been proposed, but very few are currently used, probably because of an unbalanced interest towards performance metrics, which do not consider assessments from both sensory and cognitive standpoints.
The objective is to build and evaluate visuo-tactile sensory substitution devices, aimed at helping visually impaired people to construct mental maps. A Tactile Mouse (TAMO) is proposed as simple and low-cost device for interacting with simple virtual objects. The novelty is to jointly consider objective/subjective measures from psychophysics, human behavior and neurophysiology to assess this device.
In this talk we attempt to answer to the following research questions: Is it possible to provide spatial knowledge to blind people with a minimalistic tactile device? Are mental maps acquired by blind people in a different manner than sighted people do? Does the capability of constructing a map depend on prior vision capabilities? Can blind people benefit from a tactile virtual reality? What is the effect of task
difficulty on tactile map perception? Can subjects reliably evaluate themselves?
We have found visuo-spatial cognitive cues in both blind and sighted people, by jointly analysing behavioural and neurophysiological brain signals while eliciting the construction of mental maps with TAMO. Our results show that the acquisition of new spatial information can be effective in blind as well as in sighted subjects, and that the context of virtual reality can be used to provide maps even with minimal information and with no training. They also show that the brain circuits involved in mental mapping may be supramodal, i.e. may be independent on the input sensory modality. This is important in the context of rehabilitation and in orientation and mobility programs, because spatial abilities can be improved with multimodal inputs. Additionally, from a behavioural standpoint we found that task difficulty significantly affects haptic sensitivity, perceived performance and perceived difficulty. Moreover subjects are able with the TAMO device to predict their own performance.
The long-term technological objective of the project is to offer portable tools for home-based improvement of spatial abilities.

Cooperative Multi-Agent Learning and Coordination for Cognitive Radio Networks

The radio spectrum is a scarce resource. Cognitive radio stretches this resource by enabling secondary stations to operate in portions of the spectrum that are reserved for primary stations but not currently used by the primary stations. As it is whenever stations share resources, coordination is a central issue in cognitive radio networks: in the absence of coordination, there may be collisions, congestion or interference, with concomitant loss of performance. Cognitive radio networks require coordination of secondary stations with primary stations (so that secondary stations should not interfere with primary stations) and of secondary stations with each other. Coordination in this setting is especially challenging because of the various types of sensing errors. This lecture proposes novel protocols that enable secondary stations to learn and teach with the goal of coordinating to achieve a round-robin Time Division Multiple Access (TDMA) schedule. These protocols are completely distributed (requiring neither central control nor the exchange of any control messages), fast (with speeds exceeding those of existing protocols), efficient (in terms of throughput and delay) and scalable. The protocols proposed rely on cooperative learning, exploiting the ability of stations to learn from and condition on their own histories while simultaneously teaching other stations about these histories. Analytic results and simulations illustrate the power of these protocols.
(Joint work with Jie Xu and Mihaela van der Schaar)